4 July 2018 Analysis of 2016 Minamiaso landslides using remote sensing and geographic information system
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Abstract
Following the Kumamoto earthquake in April 2016, landslides occurred in the Minamiaso village of Japan. Classification of remotely sensed imagery has been useful for mapping of the landslide area. Many studies have focused on using object-based classification of the imagery to derive landslide maps. Also, those studies used shape or texture analysis, which requires significant time and labor, and expensive commercial software. The purpose of this research was to develop a methodology to map the landslide area rapidly and economically using object-based classification with true color satellite images. Publicly available, RapidEye images taken before and after the landslide were used to save cost. Segmentation of the imagery was completed with the mean shift algorithm in the open source noncommercial remote sensing software, Orfeo toolbox. In addition, a maximum likelihood classification tool was built in a Python framework (open source freeware) to classify the objects created from Orfeo toolbox. For initial classification of each RapidEye image for both pre- and post-landslide, the overall accuracies were 91% and 88%, and Kappa statistics were 0.89 and 0.85, respectively. The landslide area was estimated as 979,815  m2 with an accuracy of 98% after human-guided refinement of the landslide features. Every procedure was implemented using open source remote sensing or geographic information system technologies, which provided cost efficiency while simultaneously providing freedom from commercial license restrictions. Also, these open source technologies provided highly flexible interoperability with a classification framework developed using Python.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jae Sung Kim, KyoHyouk Kim, "Analysis of 2016 Minamiaso landslides using remote sensing and geographic information system," Journal of Applied Remote Sensing 12(3), 036001 (4 July 2018). https://doi.org/10.1117/1.JRS.12.036001 . Submission: Received: 23 November 2017; Accepted: 15 June 2018
Received: 23 November 2017; Accepted: 15 June 2018; Published: 4 July 2018
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